The design and manufacture of new products is becoming an increasingly complex activity because users of such products are demanding an increased number of high performance features that require advanced signal processing and feedback control. Many industries today also rely on complex processes to manufacture a product that also require advanced signal processing and feedback control.
Designing advanced signal processing and control products for dynamic systems is a complicated process. The process involves complex computational algorithms, a complex sequence of design tasks, difficult decision-making, data management, data visualization, and project planning.
This situation presents a challenge for those designing products and control systems, in part because the standard methodologies and apparatuses for design of products and control systems are inadequate in several respects. Many modern products and manufacturing processes are too complex and cannot be satisfactorily controlled by standard control methods and apparatuses. Typical prior (or standard) design methods and apparatuses are linear plans that do not provide alternatives required by the uncertainty of outcomes of computations and tests, and then permit planning based on resource utilization. Also, these prior methods and apparatuses do not incorporate actual experienced results of process execution and adjust projections accordingly.
It is difficult, if not impossible, to achieve satisfactory results using prior methods and apparatuses. There are many problems in applying such methods and apparatuses to complicated manufacturing processes or control of behavior of high performance products.
Prior methods and apparatuses implemented in design tools are not effective for several reasons. For example, prior design tools typically automate linear fragments of the design activity. Results of design steps are thus unknown or uncertain before the steps are actually carried out. For instance compute time, computational errors, and exceptions, and results of physical tests cannot be known in advance to aid in decision making. These prior tools require a user to make a large number of complex decisions that depend on results of many previous steps. This is a disadvantage because the user must usually possess specific knowledge or skills in order to properly use the information gained from execution of previous steps. It is a further disadvantage because intelligent decisions can only be made and incorporated after waiting for execution of design steps. No problem-specific guidance is available from prior tools for projecting with any accuracy what the results of design steps will be.
Prior design steps can become infeasible or highly optimal because of a user decision made many steps back. Prior design tools cannot help the user see future implications of current decisions. For these reasons, with prior tools, the user must learn by problem-specific experience, over a long period of time, to resolve unknowns and dependencies across design steps.
Effective execution of this process requires expertise in signal processing and/or control theory, in-depth knowledge about the characteristics of the system to be compensated through the product, and awareness of cost-benefit tradeoffs at different stages of the design. Meeting these requirements simultaneously becomes a difficult task.
This problem is further exacerbated by a current division and separation of skill sets among those involved in the design process. For instance, control experts often do not have an in-depth knowledge of the process they are seeking to control. In addition, the proprietary nature of the processes often does not allow for acquisition of an in-depth knowledge of the process. On the other hand, process experts may know conventional control methods but do not know advanced control methods. Existing control design tools are designed for control experts, but are not suitable for process experts.
Experienced control scientists have found ways to sidestep or solve these problems in specific cases. Significant shortcomings still surface, however, when less experienced control designers or team members from other disciplines apply existing software tools to manufacturing and design problems. Consequently, current tools are inadequate for widespread use.
Most existing software design tools simply automate fragments of standard design methods. In general, the tools are ineffective when applied to control of manufacturing processes for the reasons described above.
Advanced signal processing and control design is typically done with the help of computational tools, such as Matlab®, available from Mathworks, Inc., Natick, Mass., with its associated toolboxes. The Matlab® environment provides a collection of functions to a user for solving certain types of tasks. These functions are not linked in a unified environment and using them requires a high level of expertise in compensator theory. At the same time, these functions do not provide integrated planning or decision support, task scheduling, or data management.